This invention relates to methods and systems for identifying and providing top-rated user-contributed entertainment content over a networked system. This invention also relates to providing contests for user-contributed entertainment content.
Online, user-contributed content has increased dramatically in popularity. An unprecedented volume of user-contributed content is being loaded onto the web daily. This volume comes from a broad cross-section of web users as millions of people are now posting content on the web. Social networking websites account for much of the traffic associated with user-contributed content.
There are several types of content that web users are contributing. One type of content is encyclopedic information. Websites, such as Wikipedia (www.wikipedia.org), allow users to write articles and explanations on numerous subjects. Another type of content is personal content. Social networking websites, such as myspace.com, allow individuals to post personal content to the world or to a network of contacts. Another type of content is opinion and editorial content. Websites, such as Blogger (www.blogger.com) and others, enable individuals to start a weblog to write or opine on any topic. Another type of content is entertainment content. With wide consumer access to digital cameras and camcorders and other digital recording devices, web users can easily post amateur video, photography, and music. Websites, such as Flickr (www.flickr.com), enable web users to post and share photos. Websites such as Youtube (www.youtube.com) enable users to post amateur films, reality videos, music videos, and many other types of video entertainment.
As millions of web users contribute videos, photos, songs, and performances, there are also millions who want to view the best user-contributed content as online entertainment. With millions of items of available content, there is a plethora of undesirable content available. The problem is finding the best online user-contributed entertainment content from a sea of undesirable content. Users are essentially “surfing” content databases to find something interesting. Most of users' time is spent previewing many video clips in hopes of finding a few entertaining clips.
One solution for identifying desirable content, is to use professional reviewers or editors. Many websites list a category of content as “Editors' Picks” containing content judged as desirable, or as quality content, by a small group of paid reviewers. This solution for identifying desirable content suffers from several drawbacks. A professional reviewer system is time intensive and costly. With millions of items of content submitted, it is impractical for a small group of professional editors to review each submission. Also, to hire a sufficient number of editors to review all content submitted is cost prohibitive. Another drawback in such a system is that entertainment reviewers are relying on the opinion of a small group of individuals to determine what the masses desire.
Another solution for identifying desirable entertainment content is a computerized review system. In a computerized review system, evaluation by humans is replaced with machines, computer software or the like. Such computerization enables an entire pool of content to be evaluated at a low cost. The obvious defect in a computerized system is inability to review content on an emotional level to assess an entertainment value.
Another solution is a peer review system. The most plentiful resource available for rating user-contributed content is the contributors and viewers themselves. The collective time of millions of contributors can be used for rating the enormous volume of user-contributed entertainment content. In such a system, a contributor or viewer is presented with a video clip or image and asked to rate the content. A website can also track viewing activities of users. Tracking activity and requesting ratings yields several groups of content. These groups include “Highest Rated”, “Most Emailed”, “Most Discussed”, and “Most Viewed”. Yet such systems suffer from several disadvantages.
One disadvantage of user review systems is the enormous potential for abuse. Because users often compete against each other, there is an inherent conflict of interest that leads to fraudulent ratings. A common practice is for a user to rate works of other contributors with low scores in an effort to boost a user's own score. Another common practice is creating multiple fraudulent accounts for a user to rate his own submitted work with a high rating from several accounts. In another practice, a user with many social contacts can ask those contacts to view his content to increase the number of views which makes it more likely that such content will be included on a “most popular” group. Additionally, posted content can achieve a fraudulent 5-star rating after only a few friends quickly rate a particular item of content as 5-star content.
Another disadvantage of user review systems is providing an accurate and reliable ranking system using reviewers who are not expert reviewers. Traditionally, user review systems have used a scalar method of rating content. For example, a reviewer is asked to rate a work on a scale of 1-10. Averaging the individual ratings from reviewers provides a consensus, but this calculation erroneously assumes that the evaluation skills of each user reviewer are equal. Such an erroneous assumption often yields misleading or inaccurate results. The scalar method also suffers from dead-ends of the scale. If a reviewer scores an item as “10” on a scale of 1 to 10, and the next reviewed item is better than the last item scored as “10,” then entered scores must be changed to compensate for the inaccuracy. Thus the scalar method asks a reviewer for an absolute score of an item without being able to simultaneously compare that item to all existing content.
Another measurement technique is a simple relative measurement scale. For example, a reviewer is asked to choose the better of A vs. B. Results are tallied from several A vs. B comparisons. While there are no dead ends with simple relative measurements, this technique less efficiently finds a consensus because it does not directly collect quantified ratings.
Another problem in identifying desirable content in the user-contributed entertainment industry is a lack of a clear content classification standard. Contributor-defined classification, or “tagging”, is the primary method of identifying a genre for a video or image. Since most users would like their content to be viewed by as many people as possible, there is a tendency to use dozens of tags to define a clip. Such “tag stuffing” makes it difficult to for viewers to search for desirable content as search results would present many clips that are incorrectly identified. This leads to a poor user experience.
Another problem with the user-contributed entertainment industry is that there is no lasting value for the contributors of content, beyond a temporary fame from other users viewing a clip. A problem for providers of websites that host user-contributed entertainment content, is that there is a limited opportunity for advertising revenue. Major advertisers are generally wary of having their advertisements appear next to dubious, random, and potentially offensive content. With no level of comfort in type and quality of user-contributed content, advertisers are reluctant to advertise on user-contributed entertainment websites.
Therefore, what is needed is an online entertainment network for user-contributed content that accurately identifies content that is high quality, top rated, and desirable for entertainment. What is further needed is such a network that provides an incentive to users to contribute desirable content and accurately rate content. What is further needed is such a network that provides a clear classification standard. What is further needed is such a network that provides broad advertising opportunities.